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Open access Jurnal SINTA 2

Prediction Sentiment Analysis Grab Reviews Using SVM Linear Based Streamlit

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 19 No. 2 (2025) · pp. 189–200

DOI
10.22146/ijccs.104924
ISSN
2460-7258, 1978-1520
Published
2025-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Advances in digital technology have accelerated the transformation of online transportation services, intensifying competition and driving innovations to enhance service quality. As a leading platform in Indonesia, Grab faces various challenges, including driver service quality, payment systems, and application stability, as reflected in user reviews on Google Play Store. This study aims to gain strategic insights by evaluating a linear kernel-based Support Vector Machine (SVM) model integrated into the Streamlit platform to predict the sentiment of Grab user reviews. Data were collected via web scraping and processed using tokenization, stopword removal, and stemming techniques to improve model accuracy. The model was implemented on an interactive Streamlit website featuring two main functionalities: sentiment prediction and plot visualization. The sentiment prediction feature presents sentiment distribution, performance metrics, a confusion matrix, and a classification report, while the visualization feature displays interactive word clouds, bar charts, and pie charts. Model evaluation reveals an accuracy of 83% in the Streamlit environment. These findings are expected to contribute to developers and stakeholders in enhancing Grab services and advancing more effective sentiment prediction methods.

Keywords

Collaboration network of the authors

10 authors · 15 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Muria Kudus University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ina Kusumawardani Alina FakhriMuria Kudus University1
Yudie IrawanMuria Kudus University1
Arif SetiawanMuria Kudus University1
Fauzia Alma Eka ArdiantiAffiliation unknown1
R. Rhoedy SetiawanAffiliation unknown1
Ghufron TamamiMuria Kudus University1
Wiwit Agus TriyantoMuria Kudus University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Prediction Sentiment Analysis Grab Reviews Using SVM Linear Based Streamlit</dc:title>
	<dc:creator>Hidayat, Muhammad Taufiq</dc:creator>
	<dc:creator>Arifin, Muhammad</dc:creator>
	<dc:creator>Muzid, Syafiul</dc:creator>
	<dc:subject xml:lang="en-US">Information System</dc:subject>
	<dc:subject xml:lang="en-US">Sentiment Prediction; SVM Linear; Streamlit; Grab; Online Transportation</dc:subject>
	<dc:description xml:lang="en-US">Advances in digital technology have accelerated the transformation of online transportation services, intensifying competition and driving innovations to enhance service quality. As a leading platform in Indonesia, Grab faces various challenges, including driver service quality, payment systems, and application stability, as reflected in user reviews on Google Play Store. This study aims to gain strategic insights by evaluating a linear kernel-based Support Vector Machine (SVM) model integrated into the Streamlit platform to predict the sentiment of Grab user reviews. Data were collected via web scraping and processed using tokenization, stopword removal, and stemming techniques to improve model accuracy. The model was implemented on an interactive Streamlit website featuring two main functionalities: sentiment prediction and plot visualization. The sentiment prediction feature presents sentiment distribution, performance metrics, a confusion matrix, and a classification report, while the visualization feature displays interactive word clouds, bar charts, and pie charts. Model evaluation reveals an accuracy of 83% in the Streamlit environment. These findings are expected to contribute to developers and stakeholders in enhancing Grab services and advancing more effective sentiment prediction methods.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/104924</dc:identifier>
	<dc:identifier>10.22146/ijccs.104924</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 19, No 2 (2025): April; 189-200</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/104924/41528</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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